class_docstr: Convert a collection of raw documents to a matrix of text-index
  features
  
  keep the order of word in document
  index start with 1, index 0 is default padding_value
  
  Parameters
  ----------
  
  embedding_size: size of embedding word to vector
code: def _update_vocab(self, vocab):
      new_words = sorted(set(vocab) - set(self.vocab))
      self.vocab = self.vocab + new_words
      n = len(self.vocab)
      self.word2index = dict(
          zip(self.vocab, range(self.ns, n + self.ns))
      )
      self.index2word = dict(
          zip(range(self.ns, n + self.ns), self.vocab)
      )
  
file_path: underthesea/underthesea/transformer/word_vector.py
imports: from functools import reduce
  from underthesea.transformer import Transformer
label_desc: Use image node_id 4 for calling the WordVectorTransformer obj's underlying member method code with example usage: obj._update_vocab(vocab) without return types